Dimension change of convolutional layer after applying the feature map
In this lecture I don't understand how the output has 1 layer after applying the feature map to the 3 layer input.
Topic feature-map cnn
Category Data Science
In the video the speaker says, "center the filter on each pixel and perform a dot product which gives one value per position." So I'm guessing it is a dot product between (L1, L2, L3) dot (F1, F2, F3) where L's are pixel values in the input and F's are whatever values are in the filter.
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